Predicting survival of patients treated with palliative radiotherapy: a systematic review
Bibliographic record
Abstract
Introduction: Clinician predicted survival (CPS) is a crucial part of palliative care but is often found to be inaccurate with most clinicians providing overestimates of survival, potentially leading to suboptimal care. The present paper reviews the literature on CPS in patients receiving palliative radiotherapy and assesses the accuracy of clinician generated survival estimates.Method: A search of Cochrane Central Register of Controlled Trials, Embase, and Ovid MEDLINE was conducted on 2 February 2018 to identify English articles analyzing the accuracy of CPS in cancer patients receiving palliative radiotherapy.Results: Seven studies were included in this review. Survival was overestimated on average, with overestimates ranging from +22.8 to +167.3 days. One study reported average underestimates of survival. No significant differences in accuracy were seen between disciplines. There was no correlation between years of experience and accuracy of CPS.Expert commentary: The incorporation of accurate CPS into treatment and family-related decisions can improve quality of life of palliative radiotherapy patients. Research is needed on survival estimates informed by prognostic tools, validation of prognostic tools specific to palliative settings, and the effects of CPS on dose fractionation and other treatment decisions.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".